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A thorough quantification of soil chemical properties is essential for assessing the engineering properties of forest soils for road design, construction, and maintenance. Here, we investigate the applicability of visible–near‐infrared (Vis–NIR) spectroscopy in conjunction with advanced statistical analysis for estimation of soil chemical properties. Sixty forest soil samples were collected and analyzed for pH, electrical conductivity (EC), CaCO3, organic matter (OM), and cation exchange capacity (CEC) with established laboratory methods. The spectral measurements were performed with a Vis–NIR spectrometer within a range of 350–2,500 nm. To estimate abovementioned soil properties from reflectance spectra, advanced statistical techniques including partial least squares regression (PLSR), hybrid partial least squares and artificial neural networks (PLS–DI–ANN) models, hybrid partial least squares and adaptive neural fuzzy inference system (PLS–DI–ANFIS) models, as well as narrow band spectral indices were applied. The obtained results ​​indicate that the PLS–DI–ANFIS models show great potential for the estimation of pH, EC, OM, and CEC from reflectance spectra and their first derivatives, exhibiting higher R2 values and lower RMSE than the other investigated models. The estimation accuracy for CaCO3, however, was low for all applied methods. The results confirm that Vis–NIR spectroscopy may be applied as a rapid and cost‐efficient alternative to standard chemical soil analysis techniques, aiding forest road design, construction, and maintenance. Core Ideas: Vis–NIR spectroscopy and machine learning improve soil chemical property estimation.The combined PLS–DI–ANFIS analysis provides the most robust estimation model.Vis–NIR spectroscopy aids the design, construction, and maintenance of forest roads. [ABSTRACT FROM AUTHOR]

Abstract: Though it is well known that vegetation affects the water balance of soils through canopy interception and evapotranspiration, its hydrological contribution to soil hydrology and stability is yet to be fully quantified. To improve understanding of this hydrological process, soil water dynamics have been monitored at three adjacent hillslopes with different vegetation covers (deciduous tree cover, coniferous tree cover, and grass cover), for nine months from December 2014 to September 2015. The monitored soil moisture values were translated into soil matric suction (SMS) values to facilitate the analysis of hillslope stability. Our observations showed significant seasonal variations in SMS for each vegetation cover condition. However, a significant difference between different vegetation covers was only evident during the winter season where the mean SMS under coniferous tree cover (83.6 kPa) was significantly greater than that under grass cover (41 kPa). The hydrological reinforcing contribution due to matric suction was highest for the hillslope with coniferous tree cover, while the hillslope with deciduous tree cover was second and the hillslope with grass cover was third. The greatest contributions for all cover types were during the summer season. During the winter season, the wettest period of the monitoring study, the additional hydrological reinforcing contributions provided by the deciduous tree cover (1.5 to 6.5 kPa) or the grass cover (0.9 to 5.4 kPa) were insufficient to avoid potential slope failure conditions. However, the additional hydrological reinforcing contribution from the coniferous tree cover (5.8 to 10.4 kPa) was sufficient to provide potentially stable hillslope conditions during the winter season. Our study clearly suggests that during the winter season the hydrological effects from both deciduous tree and grass covers are insufficient to promote slope stability, while the hydrological reinforcing effects from the coniferous tree cover are sufficient even during the winter season. Copyright © 2018 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: An experimental campaign was set up to quantify the contribution of evapotranspiration fluxes on hillslope hydrology and stability for different forest vegetation cover types. Three adjacent hillslopes, respectively, covered by hardwood, softwood, and grass were instrumented with nine access tubes each to monitor soil water dynamics at the three depths of 30, 60, and 100 cm, using a PR2/6 profile probe (Delta‐T Devices Ltd) for about 6 months including wet periods. Soil was drier under softwood and wetter under grass at all the three depths during most of the monitoring period. Matric suction derived via the soil moisture measurements was more responsive to changes in the atmospheric conditions and also recovered faster at the 30 cm depth. Results showed no significant differences between mean matric suction under hardwood (101.6 kPa) with that under either softwood or grass cover. However, a significant difference was found between mean matric suction under softwood (137.5 kPa) and grass (84.3 kPa). Results revealed that, during the wettest period, the hydrological effects from all three vegetation covers were substantial at the 30 cm depth, whereas the contribution from grass cover at 60 cm (2.0 kPa) and 100 cm (1.1 kPa) depths and from hardwood trees at 100 cm depth (1.2 kPa) was negligible. It is surmised that potential instability would have occurred at these larger depths along hillslopes where shallow hillslope failures are most likely to occur in the region. The hydrological effects from softwood trees, 8.1 and 3.9 kPa, were significant as the corresponding factor of safety values showed stable conditions at both depths of 60 and 100 cm, respectively. Therefore, the considerable hydrological reinforcing effects from softwood trees to the 100 cm depth suggest that a hillslope stability analysis would show that hillslopes with softwood trees will be stable even during the wet season. [ABSTRACT FROM AUTHOR]